It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...) is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an (...) illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment. (shrink)

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also (...) unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. (shrink)

Much has been written about the free energy principle (FEP), and much misunderstood. The principle has traditionally been put forth as a theory of brain function or biological self-organisation. Critiques of the framework have focused on its lack of empirical support and a failure to generate concrete, falsifiable predictions. I take both positive and negative evaluations of the FEP thus far to have been largely in error, and appeal to a robust literature on scientific modelling to rectify the situation. A (...) prominent account of scientific modelling distinguishes between model structure and model construal. I propose that the FEP be reserved to designate a model structure, to which philosophers and scientists add various construals, leading to a plethora of models based on the formal structure of the FEP. An entailment of this position is that demands placed on the FEP that it be falsifiable or that it conform to some degree of biological realism rest on a category error. To this end, I deliver first an account of the phenomenon of model transfer and the breakdown between model structure and model construal. In the second section, I offer an overview of the formal elements of the framework, tracing their history of model transfer and illustrating how the formalism comes apart from any interpretation thereof. Next, I evaluate existing comprehensive critical assessments of the FEP, and hypothesise as to potential sources of existing confusions in the literature. In the final section, I distinguish between what I hold to be the FEP—taken to be a modelling language or modelling framework—and what I term “FEP models.”. (shrink)

The free energy principle says that any self-organising system that is at nonequilibrium steady-state with its environment must minimize its free energy. It is proposed as a grand unifying principle for cognitive science and biology. The principle can appear cryptic, esoteric, too ambitious, and unfalsifiable—suggesting it would be best to suspend any belief in the principle, and instead focus on individual, more concrete and falsifiable ‘process theories’ for particular biological processes and phenomena like perception, decision and action. Here, I explain (...) the free energy principle, and I argue that it is best understood as offering a conceptual and mathematical analysis of the concept of existence of self-organising systems. This analysis offers a new type of solution to long-standing problems in neurobiology, cognitive science, machine learning and philosophy concerning the possibility of normatively constrained, self-supervised learning and inference. The principle can therefore uniquely serve as a regulatory principle for process theories, to ensure that process theories conforming to it enable self-supervision. This is, at least for those who believe self-supervision is a foundational explanatory task, good reason to believe the free energy principle. (shrink)

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology (...) and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

Recent work in computational and cognitive neuroscience depicts the brain as an ever‐active prediction machine: an inner engine continuously striving to anticipate the incoming sensory barrage. I briefly introduce this class of models before contrasting two ways of understanding the implied vision of mind. One way (Conservative Predictive Processing) depicts the predictive mind as an insulated inner arena populated by representations so rich and reconstructive as to enable the organism to ‘throw away the world’. The other (Radical Predictive Processing) stresses (...) the use of fast and frugal, action‐involving solutions of the kind highlighted by much work in robotics and embodied cognition. But it goes further, by showing how predictive schemes can combine frugal and more knowledge‐intensive strategies, switching between them fluently and continuously as task and context dictate. I end by exploring some parallels with work in enactivism, and by noting a certain ambivalence concerning internal representations and their role in the predictive mind. (shrink)

We distinguish between three philosophical views on the neuroscience of predictive models: predictive coding, predictive processing and predictive engagement. We examine the concept of active inference under each model and then ask how this concept informs discussions of social cognition. In this context we consider Frith and Friston’s proposal for a neural hermeneutics, and we explore the alternative model of enactivist hermeneutics.

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...) representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them. (shrink)

I discuss Clark’s predictive processing/extended mind hybrid, diagnosing a problem: Clark’s hybrid suggests that, when we use them, we pay attention to mind-extending external resources. This clashes with a commonly accepted necessary condition of cognitive extension; namely, that mind-extending resources must be phenomenally transparent when used. I then propose a solution to this problem claiming that the phenomenal transparency condition should be rejected. To do so, I put forth a parity argument to the effect that phenomenal transparency cannot be a (...) necessary condition on cognitive extension: roughly, since internal cognitive resources can fail to be transparent when used, by parity, external resources can fail to be phenomenally transparent too. Further, I argue that phenomenal transparency is not even a reliable indicator of cognitive extension; hence its absence should not be considered a problem for Clark’s extended mind-predictive processing hybrid. Lastly, I consider and allay a number of worries my proposal might raise, and conclude the paper. (shrink)

A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...) explained within the Bayesian framework. As a forewarning, progress in cognitive science may be hindered if too many scientists continue to focus their efforts on Bayesian modelling, which risks to monopolize scientific resources that may be better allocated to alternative approaches. (shrink)

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, (...) we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. (shrink)

‘Predictive Processing’ is an emerging paradigm in cognitive neuroscience that depicts the human mind as an uncertainty management system that constructs probabilistic predictions of sensory s...

Biological brains are increasingly cast as ‘prediction machines’: evolved organs whose core operating principle is to learn about the world by trying to predict their own patterns of sensory stimulation. This, some argue, should lead us to embrace a brain-bound ‘neurocentric’ vision of the mind. The mind, such views suggest, consists entirely in the skull-bound activity of the predictive brain. In this paper I reject the inference from predictive brains to skull-bound minds. Predictive brains, I hope to show, can be (...) apt participants in larger cognitive circuits. The path is thus cleared for a new synthesis in which predictive brains act as entry-points for ‘extended minds’, and embodiment and action contribute constitutively to knowing contact with the world. (shrink)

Does perceptual consciousness require cognitive access? Ned Block argues that it does not. Central to his case are visual memory experiments that employ post-stimulus cueing—in particular, Sperling's classic partial report studies, change-detection work by Lamme and colleagues, and a recent paper by Bronfman and colleagues that exploits our perception of ‘gist’ properties. We argue contra Block that these experiments do not support his claim. Our reinterpretations differ from previous critics' in challenging as well a longstanding and common view of visual (...) memory as involving declining capacity across a series of stores. We conclude by discussing the relation of probabilistic perceptual representations and phenomenal consciousness. (shrink)

This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can (...) be shown to be the same as covariance (through a set of additional steps). It is well known that facts about covariance do not entail facts about representational content. So there is no reason to think that the KL-divergence is a measure of (mis-)representational content. This paper thus provides an enactive, non-representational account of Bayesian belief optimisation in hierarchical PP. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

According to the free energy principle biological agents resist a tendency to disorder in their interactions with a dynamically changing environment by keeping themselves in sensory and physiological states that are expected given their embodiment and the niche they inhabit :127–138, 2010. doi: 10.1038/nrn2787). Why would a biological agent that aims at minimising uncertainty in its encounters with the world ever be motivated to seek out novelty? Novelty for such an agent would arrive in the form of sensory and physiological (...) states that are unexpected. Such an agent ought therefore to avoid novel and surprising interactions with the world one might think. Yet humans and many other animals find play and other forms of novelty-seeking and exploration hugely rewarding. How can this be understood in frameworks for studying the mind that emphasise prediction error minimisation? This problem has been taken up in recent research concerned with epistemic action—actions an agent engages in to reduce uncertainty. However that work leaves two questions unanswered, which it is the aim of our paper to address. First, no account has been given yet of why it should feel good to the agent to engage the world playfully and with curiosity. Second an appeal is made to precision-estimation to explain epistemic action, yet it remains unclear how precision-weighting works in action more generally, or active inference. We argue that an answer to both questions may lie in the bodily states of an agent that track the rate at which free energy is being reduced. The recent literature on the predictive brain has connected the valence of emotional experiences to the rate of change in the reduction of prediction error :e1003094, 2013. doi: 10.1371/journal.pcbi.1003094; Van de Cruys, in Metzinger and Wiese Philosophy and predictive processing, vol 24, MIND Group, Frankfurt am Main, 2017. doi: 10.15502/9783958573253). In this literature valenced emotional experiences are hypothesised to be identical with changes in the rate at which prediction error is reduced. Experiences are negatively valenced when overall prediction error increases and are positively valenced when the sum of prediction errors decrease. We offer an ecological-enactive interpretation of the concept of valence and its connection to rate of change of prediction error. We show how rate of change should be understood in terms of embodied states of affordance-related action readiness. We then go on to apply this ecological-enactive account of error dynamics to provide an answer to the first question we have raised: It may explain why it should feel good to an agent to be curious and playful. Our ecological-enactive account also allows us to show how error dynamics may provide an answer to the second question we have raised regarding how precision-weighting works in active inference. An agent that is sensitive to rates of error reduction can tune precision on the fly. We show how this ability to tune precision on the go can allow agents to develop skills for adapting better and better to the unexpected, and search out opportunities for resolving uncertainty and progressing in its learning. (shrink)

Does the material basis of conscious experience extend beyond the boundaries of the brain and central nervous system? In Clark 2009 I reviewed a number of ‘enactivist’ arguments for such a view and found none of them compelling. Ward (2012) rejects my analysis on the grounds that the enactivist deploys an essentially world-involving concept of experience that transforms the argumentative landscape in a way that makes the enactivist conclusion inescapable. I present an alternative (prediction-and-generative-model-based) account that neatly accommodates all the (...) positive evidence that Ward cites on behalf of this enactivist conception, and that (I argue) makes richer and more satisfying contact with the full sweep of human experience. (shrink)

Bortolotti’s Delusions and Other Irrational Beliefs defends the view that delusions are beliefs on a continuum with other beliefs. A different view is that delusions are more like illusions, that is, they arise from faulty perception. This view, which is not targeted by the book, makes it easier to explain why delusions are so alien and disabling but needs to appeal to forensic aspects of functioning.

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That means that (...) the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

Arguments for the ‘extended mind’ seem to suggest the possibility of ‘extended knowers’—agents whose specifically epistemic virtues may depend on systems whose boundaries are not those of the brain or the biological organism. Recent discussions of this possibility invoke insights from virtue epistemology, according to which knowledge is the result of the application of some kind of cognitive skill or ability on the part of the agent. In this paper, I argue that there is a fundamental tension in these appeals (...) to cognitive virtue. The tension centers on the presence of a tool or technology as an object of awareness, hence something apt for epistemically virtuous engagement on the part of the agent. I highlight a dilemma: the better something looks as a non-biological element of the machinery of mind, the worse it looks as a potential object of any specifically epistemic skill or ability on the part of the agent. The tension is resolved, I argue, by thinking about sub-personal forms of epistemic hygiene. I examine one such form , and show how it sits neatly with the vision of the extended mind. I end by asking what we can still reasonably expect, given this more complex sub-personal story, by way of agent-level cognitive hygiene. (shrink)

Events—the experiences we think we are having and recall having had—are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event‐rendering skills acquired and how do (...) event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of “troughs” in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable‐unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information‐optimization account, a fluent event‐processor predicts such troughs and selectively attends to them—while suppressing attention to other regions—as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information‐rich portions of the unfolding flow of sensation. Such findings point to the development of event‐processing fluency—whether in action, language, or other domains—depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration. (shrink)

Free Energy Principle underlies a unifying framework that integrates theories of origins of life, cognition, and action. Recently, FEP has been developed into a Markovian monist perspective. The paper expresses scepticism about the validity of arguments for Markovian monism. The critique is based on the assumption that Markovian models are scientific models, and while we may defend ontological theories about the nature of scientific models, we could not read off metaphysical theses about the nature of target systems from our theories (...) of nature of scientific models. The paper draws attention to different ways of understanding Markovian models, as material entities, fictional entities, and mathematical structures. I argue that none of these interpretations contributes to the defence of a metaphysical stance. This is because scientific representation is a sophisticated process, and properties of Markovian models—such as the property of being neither physical nor mental—could not be projected onto their targets to determine the ontological properties of targets easily. (shrink)

There is surprising evidence that introspection of our phenomenal states varies greatly between individuals and within the same individual over time. This puts pressure on the notion that introspection gives reliable access to our own phenomenology: introspective unreliability would explain the variability, while assuming that the underlying phenomenology is stable. I appeal to a body of neurocomputational, Bayesian theory and neuroimaging findings to provide an alternative explanation of the evidence: though some limited testing conditions can cause introspection to be unreliable, (...) mostly it is our phenomenology itself that is variable. With this account of phenomenal variability, the occurrence of the surprising evidence can be explained while generally retaining introspective reliability. (shrink)

What is the primary function of consciousness in the nervous system? The answer to this question remains enigmatic, not so much because of a lack of relevant data, but because of the lack of a conceptual framework with which to interpret the data. To this end, we have developed Passive Frame Theory, an internally coherent framework that, from an action-based perspective, synthesizes empirically supported hypotheses from diverse fields of investigation. The theory proposes that the primary function of consciousness is well-circumscribed, (...) serving the 'somatic nervous system[. For this system, consciousness serves as a frame that constrains and directs skeletal muscle output, thereby yielding adaptive behavior. The mechanism by which consciousness achieves this is more counterintuitive, passive, and “low level” than the kinds of functions that theorists have previously attributed to consciousness. Passive frame theory begins to illuminate (a) what consciousness contributes to nervous function, (b) how consciousness achieves this function, and (c) the neuroanatomical substrates of conscious processes. Our untraditional, action-based perspective focuses on olfaction instead of on vision and is 'descriptive' (describing the products of nature as they evolved to be) rather than 'normative' (construing processes in terms of how they should function). Passive frame theory begins to isolate the neuroanatomical, cognitive-mechanistic, and representational (e.g., conscious contents) processes associated with consciousness. (shrink)

This paper examines the relationship between perceiving and imagining on the basis of predictive processing models in neuroscience. Contrary to the received view in philosophy of mind, which holds that perceiving and imagining are essentially distinct, these models depict perceiving and imagining as deeply unified and overlapping. It is argued that there are two mutually exclusive implications of taking perception and imagination to be fundamentally unified. The view defended is what I dub the ecological–enactive view given that it does not (...) succumb to internalism about the mind-world relation, and allows one to keep a version of the received view in play. (shrink)

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This the final submitted version of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

According to one theory, the brain is a sophisticated hypothesis tester: perception is Bayesian unconscious inference where the brain actively uses predictions to test, and then refine, models about what the causes of its sensory input might be. The brain’s task is simply continually to minimise prediction error. This theory, which is getting increasingly popular, holds great explanatory promise for a number of central areas of research at the intersection of philosophy and cognitive neuroscience. I show how the theory can (...) help us understand striking phenomena at three cognitive levels: vision, sensory integration, and belief. First, I illustrate central aspects of the theory by showing how it provides a nice explanation of why binocular rivalry occurs. Then I suggest how the theory may explain the role of the unified sense of self in rubber hand and full body illusions driven by visuotactile conflict. Finally, I show how it provides an approach to delusion formation that is consistent with one-deficit accounts of monothematic delusions. (shrink)

In this paper we argue that predictive processing (PP) theory cannot account for the phenomenon of affect-biased attention prioritized attention to stimuli that are affectively salient because of their associations with reward or punishment. Specifically, the PP hypothesis that selective attention can be analyzed in terms of the optimization of precision expectations cannot accommodate affect-biased attention; affectively salient stimuli can capture our attention even when precision expectations are low. We review the prospects of three recent attempts to accommodate affect with (...) tools internal to PP theory: Miller and Clark’s (2018) embodied inference; Seth’s (2013) interoceptive inference; and Joffily and Coricelli’s (2013) rate of change of free energy. In each case we argue that the account does not resolve the challenge from affect-biased attention. For this reason, we conclude that prediction error minimization is not sufficient to explain all mental phenomena, contrary to the claim that the PP framework provides a unified theory of all mental phenomena or the brain ‘s cognitive functioning. Nevertheless, we suggest that empirical investigation of the interaction between affective salience and precision expectations should prove helpful in understanding the limits of PP theory, and may provide new directions for the application of a Bayesian perspective to perception. (shrink)

The frame problem refers to the fact that organisms must be able to zero in on relevant aspects of the world and intelligently ignore the vast majority of the world that is irrelevant to their goals. In this paper we aim to point out the connection between two leading frameworks for thinking about how organisms achieve this. Predictive processing is a rapidly growing framework within cognitive science which suggests that organisms assign a high ‘weight’ to relevant aspects of the world, (...) effectively treating relevant aspects of the world as if they are more precise. This assignment of weight is called precision-weighting and is the predictive processing account of how organisms allocate their attention. Relevance Realization is a framework that conceptualizes an organism’s ability to realize relevance as resulting from a dynamical system in which a cognitive agent makes use of opponent processing relationships to zero in on relevant aspects of the world. In this paper we use recent work on the diametric model of autism and psychosis to demonstrate that the tradeoffs inherent to precision-weighting are also inherent to relevance realization. This connection will demonstrate that although these frameworks have a different intellectual background and use a different set of concepts and vocabulary, they are both pointing to the same underlying process. The fact that these different frameworks have converged on such a similar solution to the problem of how organisms realize relevance serves to demonstrate the plausibility of that solution. (shrink)

This paper considers the Cartesian theatre as a metaphor for the virtual reality models that the brain uses to make inferences about the world. This treatment derives from our attempts to understand dreaming and waking consciousness in terms of free energy minimization. The idea here is that the Cartesian theatre is not observed by an internal audience but furnishes a theatre in which fictive narratives and fantasies can be rehearsed and tested against sensory evidence. We suppose the brain is driven (...) by the imperative to infer the causes of its sensory samples; in much the same way as scientists are compelled to test hypotheses about experimental data. This recapitulates Helmholtz's notion of unconscious inference and Gregory's treatment of perception as hypothesis testing. However, we take this further and consider the active sampling of the world as the gathering of confirmatory evidence for hypotheses based on our virtual reality. The ensuing picture of consciousness resolves a number of seemingly hard problems in consciousness research and is internally consistent with current thinking in systems neuroscience and theoretical neurobiology. In this formalism, there is a dualism that distinguishes between the process of inference and the process that entails inference. This separation is reflected by the distinction between beliefs and the physical brain states that encode them. This formal approach allows us to appeal to simple but fundamental theorems in information theory and statistical thermodynamics that dissolve some of the mysterious aspects of consciousness. (shrink)

Information is a central notion for cognitive sciences and neurosciences, but there is no agreement on what it means for a cognitive system to acquire information about its surroundings. In this paper, we approximate three influential views on information: the one at play in ecological psychology, which is sometimes called information for action; the notion of information as covariance as developed by some enactivists, and the idea of information as minimization of uncertainty as presented by Shannon. Our main thesis is (...) that information for action can be construed as covariant information, and that learning to perceive covariant information is a matter of minimizing uncertainty through skilled performance. We argue that the agent’s cognitive system conveys information for acting in an environment by minimizing uncertainty about how to achieve her intended goals in that environment. We conclude by reviewing empirical findings that support our view and by showing how direct learning, seen as instance of ecological rationality at work, is how mere possibilities for action are turned into embodied know-how. Finally, we indicate the affinity between direct learning and sense-making activity. (shrink)

Paweł Gładziejewski has recently argued that the framework of predictive processing postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representational role. The goal of this paper is to promote this discussion by exploring the contents of representations posited by PP. Gładziejewski already points out that structural theories of representational content can successfully be applied to PP. Here, I propose to make the treatment slightly more rigorous by invoking Francis Egan’s distinction between (...) mathematical and cognitive contents. Applying this distinction to representational contents in PP, I first show that cognitive contents in PP are determined by mathematical contents, at least in the sense that computational descriptions in PP put constraints on ascriptions of cognitive contents. After that, I explore to what extent these constraints are specific. I argue that the general mathematical contents posited by PP do not constrain ascriptions of cognitive content in a specific way. However, there are at least three aspects of PP that constrain ascriptions of cognitive contents in more specific ways: formal PP models posit specific mathematical contents that define more specific constraints; PP entails claims about how computational mechanisms underpin cognitive phenomena ; the processing hierarchy posited by PP goes along with more specific constraints. (shrink)

The new mechanists and the autonomy approach both aim to account for how biological phenomena are explained. One identifies appeals to how components of a mechanism are organized so that their activities produce a phenomenon. The other directs attention towards the whole organism and focuses on how it achieves self-maintenance. This paper discusses challenges each confronts and how each could benefit from collaboration with the other: the new mechanistic framework can gain by taking into account what happens outside individual mechanisms, (...) while the autonomy approach can ground itself in biological research into how the actual components constituting an autonomous system interact and contribute in different ways to realize and maintain the system. To press the case that these two traditions should be constructively integrated we describe how three recent developments in the autonomy tradition together provide a bridge between the two traditions: (1) a framework of work and constraints, (2) a conception of function grounded in the organization of an autonomous system, and (3) a focus on control. (shrink)

In this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, some of (...) the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective. (shrink)

An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind–brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.

Cognitive systems research has predominantly been guided by the historical distinction between emotion and cognition, and has focused its efforts on modelling the “cognitive” aspects of behaviour. While this initially meant modelling only the control system of cognitive creatures, with the advent of “embodied” cognitive science this expanded to also modelling the interactions between the control system and the external environment. What did not seem to change with this embodiment revolution, however, was the attitude towards affect and emotion in cognitive (...) science. This paper argues that cognitive systems research is now beginning to integrate these aspects of natural cognitive systems into cognitive science proper, not in virtue of traditional “embodied cognitive science”, which focuses predominantly on the body’s gross morphology, but rather in virtue of research into the interoceptive, organismic basis of natural cognitive systems. (shrink)

According to predictive processing, an increasingly influential paradigm in cognitive science, the function of the brain is to minimize the prediction error of its sensory input. Conceptual engineering is the practice of assessing and changing concepts or word meanings. We contribute to both strands of research by proposing the first cognitive account of conceptual engineering, using the predictive processing framework. Our model reveals a new kind of implementation problem as prediction errors are only minimized if enough agents embrace conceptual changes. (...) This problem can be overcome by emphasizing the importance ofsocial normsandconceptual pluralism. (shrink)

The subject of musical emotions has emerged only recently as a major area of research. While much work in this area offers fascinating insights to musicological research, assumptions about the nature of emotional experience seem to remain committed to appraisal, representations, and a rule-based or information-processing model of cognition. Over the past three decades alternative ‘embodied’ and ‘enactive’ models of mind have challenged this approach by emphasising the self-organising aspects of cognition, often describing it as an ongoing process of dynamic (...) interactivity between an organism and its environment. More recently, this perspective has been applied to the study of emotion in general, opening up interesting new possibilities for theory and research. This new approach, however, has received rather limited attention in musical contexts. With this in mind, we critically review the history of music and emotion studies, arguing that many existing theories offer only limited views of what musical-emotional experience entails. We then attempt to provide preliminary grounding for an alternative perspective on music and emotion based on the enactive/dynamic systems approach to the study of mind. (shrink)

Which is my body and how do I distinguish it from the bodies of others, or from objects in the surrounding environment? The perception of our own body and more particularly our sense of body ownership is taken for granted. Nevertheless experimental findings from body ownership illusions (BOIs), show that under specific multisensory conditions, we can experience artificial body parts or fake bodies as our own body parts or body respectively. The aim of the present paper is to discuss how (...) and why BOIs are induced. We review several experimental findings concerning the spatial, temporal and semantic principles of crossmodal stimuli that have been applied to induce BOIs. On the basis of these principles, we discuss theoretical approaches concerning the underlying mechanism of BOIs. We propose a conceptualization based on Bayesian causal inference for addressing how our nervous system could infer whether an object belongs to our own body, using multisensory, sensorimotor and semantic information, and we discuss how this can account for several experimental findings. Finally, we point to neural network models as an implementational framework within which the computational problem behind BOIs could be addressed in the future. (shrink)